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Functional Python Programming

You're reading from   Functional Python Programming Discover the power of functional programming, generator functions, lazy evaluation, the built-in itertools library, and monads

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788627061
Length 408 pages
Edition 2nd Edition
Languages
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Toc

Table of Contents (18) Chapters Close

Preface 1. Understanding Functional Programming FREE CHAPTER 2. Introducing Essential Functional Concepts 3. Functions, Iterators, and Generators 4. Working with Collections 5. Higher-Order Functions 6. Recursions and Reductions 7. Additional Tuple Techniques 8. The Itertools Module 9. More Itertools Techniques 10. The Functools Module 11. Decorator Design Techniques 12. The Multiprocessing and Threading Modules 13. Conditional Expressions and the Operator Module 14. The PyMonad Library 15. A Functional Approach to Web Services 16. Optimizations and Improvements 17. Other Books You May Enjoy

Writing pure functions


A function with no side effects fits the pure mathematical abstraction of a function: there are no global changes to variables. If we avoid the global statement, we will almost meet this threshold. To be pure, a function should also avoid changing the state mutable objects.

Here's an example of a pure function:

def m(n: int) -> int:
    return 2**n-1

This result depends only on the parameter, n. There are no changes to global variables and the function doesn't update any mutable data structures.

Any references to values in the Python global namespace (using a free variable) is something we can rework into a proper parameter. In most cases, it's quite easy. Here is an example that depends on a free variable:

def some_function(a: float, b: float, t: float) -> float:
    return a+b*t+global_adjustment

We can refactor this function to turn the global_adjustment variable into a proper parameter. We would need to change each reference to this function, which may have a large...

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